Fixed reference frame phase-locked loop (FRF-PLL) for unbalanced line voltage conditions

ABSTRACT

The present invention is an algorithm to implement a phase-locked loop (PLL) which is able to provide an estimation of the angular frequency, and both the positive and negative sequences of the fundamental component of a three-phase signal. These sequences are provided in fixed reference frame coordinates, and thus the proposed algorithm is referred as fixed reference frame PLL (FRF-PLL). In fact, the FRF-PLL does not require transformation of variables into the synchronous reference frame coordinates as in most PLL schemes. The detection of the positive sequence component of the source voltage at fundamental frequency is essential for the control and synchronization of systems coupled with the electric network, which are required to run even under grid disturbances such as unbalanced voltages, voltages sags, harmonic distortion and angular frequency variations. The design of the FRF-PLL is based on a complete description of the source voltage involving both positive and negative sequences in stationary coordinates and considering that the angular frequency is uncertain. Therefore the FRF-PLL is intended to perform properly under unbalanced conditions, and to be robust against angular frequency variations, providing a fast and precise response.

FIELD OF THE INVENTION

The present invention is related with the design of phase-locked loops (PLL) algorithms which are able to provide synchronization between two periodic signals by reducing the difference of the phase angle between them, and thus providing a detection mechanism to reconstruct the phase angle and the angular frequency of the reference signal. Although PLLs have been widely used in many electronic applications, the PLL presented here is of special interest in the synchronization of power electronic equipment coupled with the electric network, such as controlled AC-DC converters, cycloconverters, active filters and other energy storage devices. In particular, the presented PLL has been designed to work in three-phase electric utility operating under unbalanced and distorted conditions.

BACKGROUND OF THE INVENTION

One of the most important aspects to consider in the control of grid-connected devices (such as controlled AC-DC converters, active filters, cycloconverters, and other energy storage systems coupled with the utility voltage) is the proper synchronization with the reference grid. The most widely accepted solution to provide this synchronization are the PLL technics. In general, a conventional PLL comprises a phase frequency detector (PFD) aimed to detect phase error and frequency error between the sensed input signal and an internally reconstructed signal. This error signal is processed in a filter and the result is used to adjust the frequency of a voltage controlled oscillator (VCO). This process continues until the angular frequency and phase angle of the internally reconstructed signal match those of the input signal. A PLL of the generic type is specified in the U.S. Pat. No. 6,008,703, which is titled “Digital compensation for wideband modulation of a phase locked loop frequency synthesizer” by M. Perrott, et al., which is incorporated by reference.

In most conventional control strategies for power electronic systems, e.g. active filters, active rectifiers, etc., both the magnitude and phase angle of the positive-sequence voltage signal are necessary. These quantities are mainly used for synchronization of the system output variables, power flux calculations or for the transformation of state variables into rotating reference frame coordinates. Regardless of the technique used in the detection process, the amplitude and the phase angle estimation of the positive-sequence component must be computed as fast and as accurate as possible despite of the unavoidable disturbances in the electric utility. Among the main disturbances are voltage harmonic distortion, voltage unbalance and angular frequency variations.

The most extended technique used for frequency-insensitive positive-sequence detection is the conventional three-phase PLL based on the synchronous reference frame (SRF-PLL). In the conventional SRF-PLL, the three-phase voltage vector is translated from the natural reference frame to the rotating reference frame by using a combination of Clarke's and Park's transformations. The angular position of this reference frame is controlled by a feedback loop which regulates the q-component of the positive sequence of the voltage to zero. Therefore in steady-state, the detected d-component depicts the voltage vector amplitude, while its phase angle is determined by the output of the feedback loop. Under ideal utility conditions, i.e., neither harmonic distortion nor unbalance is present, a high band-width feedback loop of the SRF-PLL yields a fast and precise detection of the phase angle and amplitude of the utility voltage vector. In case the utility voltage is distorted with high-order harmonics, the bandwidth of the SRF-PLL feedback loop can be reduced to achieve a satisfactory operation, i.e., to reject and cancel out the effect of the harmonics on the output. However, the PLL bandwidth reduction is not an acceptable solution in the presence of unbalanced grid voltages.

The operation of the conventional SRF-PLL is reported in V. Kaura and V. Blasco, “Operation of a phase locked loop system under distorted utility conditions,” IEEE Trans. on Ind. Electr., Vol. 33, No. 1, pp. 58-62, January/February 2007; and in P. Rodríguez, et al., “Decoupled double synchronous reference frame PLL for Power converters control,” IEEE Trans. on Ind. Electr., Vol. 22, No. 2, pp. 584-592, March 2007, which are incorporated by reference.

Other possibilities for the positive-sequence detection are based on the instantaneous symmetrical components (ISC), on space vector filters (SVF), and on the recursive weighted least-square estimation algorithm (WLSE), which are reported in A. Ghosh, et al., “A new algorithm for the generation of reference voltages of a DVR using the method of instantaneous symmetrical components,” IEEE Power Eng. Rev., Vol. 22, No. 1, pp. 63-65, January 2002; J. Svensson, “Synchronisation methods for grid connected voltage source converters,” Proc. Inst. Electr. Eng., Vol. 148, No. 1, pp. 229-235, May. 2001; and H. Song, et al., “An instantaneous phase angle detection algorithm under unbalanced line voltage condition,” Proc. IEEE Power Electron. Spec. Conf., Vol. 1, pp. 533-537, August 1999.

SUMMARY OF THE INVENTION

In this document a PLL scheme is presented which is aimed to estimate the angular frequency, and both positive and negative sequences of the fundamental component of an unbalanced three-phase signal. The proposed scheme delivers the estimates of both sequences in fixed frame coordinates, and does not require the transformation of variables to the synchronous reference frame as in most conventional PLLs. This is the reason why the proposed PLL received the name of fixed reference frame PLL (FRF-PLL). The proposed FRF-PLL scheme is designed to properly operate under severe unbalanced conditions and is robust against angular frequency changes in the three-phase source voltage signal. Although not considered in the design, it is shown that the scheme is also robust against harmonic distortion present in the source voltage signal.

The idea behind the design consists in:

(i) Propose a model that can accurately reproduce the evolution of an unbalanced three-phase signal. For this purpose both positive and negative sequence components of the three-phase signal are considered. Most PLLs reported in the literature consider the positive sequence only. In other words, they are all based on the model description of the three-phase line voltage signal {dot over (ν)}_(αβ)=Jων_(αβ), which holds for the balanced case only. In contrast, the present work introduces a new auxiliary variable φ_(αβ), which permits to describe an unbalanced three-phase line voltage signal completely. To realize this, notice that the voltage signal can be represented as the sum of both sequences, i.e., ν_(αβ)=ν_(αβ) ^(p)+ν_(αβ) ^(n), and define the auxiliary variable as the difference between both sequences, i.e., φ_(αβ)=ν_(αβ) ^(p)−ν_(αβ) ^(n). Out of this, the three-phase signal ν_(αβ) can be fully described by the system {dot over (ν)}_(αβ)=Jωφ_(αβ), {dot over (φ)}_(αβ)=Jων_(αβ). (ii) Based on the proposed model, a state estimator is designed to reconstruct both positive and negative sequences of the three-phase signal. The state estimator is built as a copy of the model with additional damping. (iii) As the estimator expression involves the angular frequency, then an adaptation law is incorporated to reconstruct the angular frequency as well.

The stability and convergence study of the proposed FRF-PLL scheme follow the Lyapunov's approach, without involving any linearization process.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 Shows a general schematic diagram of the connection of the FRF-PLL system to the utility grid.

FIG. 2 Shows a detailed block diagram of the proposed FRF-PLL algorithm.

FIG. 3 Shows a transient response of the conventional SRF-PLL when the utility voltage goes from a balanced to an unbalanced condition. (from top to bottom) Utility voltage ν₁₂₃, estimated phase angle {circumflex over (θ)}, estimated frequency {circumflex over (ω)}, and estimated positive-sequence voltage in the synchronous reference frame {circumflex over (ν)}_(dq) ^(p).

FIG. 4 Shows a transient response of the proposed FRF-PLL when the utility voltage passes from a balanced to an unbalanced condition. (from top to bottom) Utility voltage ν₁₂₃, estimated phase angle {circumflex over (θ)}, estimated angular frequency {circumflex over (ω)}, and estimated positive-sequence voltage {circumflex over (ν)}₁₂₃ ^(p).

FIG. 5 Shows a response of the proposed FRF-PLL to an unbalanced distorted utility voltage during start-up. (from top to bottom) Utility voltage ν₁₂₃, estimated phase angle {circumflex over (θ)}, estimated angular frequency {circumflex over (ω)}, and estimated positive-sequence voltage {circumflex over (ν)}₁₂₃ ^(p).

FIG. 6 Shows a transient response of the proposed FRF-PLL to a utility frequency change from 50 Hz to 35 Hz. (from top to bottom) Utility voltage ν₁₂₃, estimated phase angle {circumflex over (θ)}, estimated angular frequency {circumflex over (ω)}, and estimated positive-sequence voltage {circumflex over (ν)}₁₂₃ ^(p).

FIG. 7 Shows a transient response of the proposed FRF-PLL to a utility frequency change from 35 Hz to 50 Hz. (from top to bottom) Utility voltage ν₁₂₃, estimated phase angle {circumflex over (θ)}, estimated angular frequency {circumflex over (ω)}, and estimated positive-sequence voltage {circumflex over (ν)}₁₂₃ ^(p).

DETAILED DESCRIPTION OF THE INVENTION

As previously mentioned, the proposed FRF-PLL scheme is aimed to estimate the angular frequency and the positive-sequence of the fundamental component of an unbalanced three-phase signal. The proposed FRF-PLL scheme is designed to operate properly under severe unbalanced conditions and to be robust against possible angular frequency changes in the three-phase signal. Although not explicitly considered in the design, the proposed FRF-PLL scheme must reduce also the effects of harmonic distortion existent in the three-phase signal.

The model. The idea behind the proposed approach is to propose a model that can reproduce the three-phase signal in the most accurately way. Then, based on this model, a state estimator plus an adaptation law for the angular frequency are designed.

In what follows, it is considered that the three-phase signal is composed only by a fundamental component, with the fundamental frequency given by ω=2πf. Moreover, it is considered that the signal, originally in three-phase coordinates, has been transformed to (fixed frame) αβ-coordinates using the following Clarke transformation

ν_(αβ)=T_(αβ)ν₁₂₃  (1)

where

${v_{\alpha\beta} = \begin{bmatrix} v_{\alpha} \\ v_{\beta} \end{bmatrix}},{v_{123} = \begin{bmatrix} v_{1} \\ v_{2} \\ v_{3} \end{bmatrix}},$

and matrix T_(αβ) is given by

$\begin{matrix} {{T_{\alpha\beta} = {\frac{2}{3}\begin{bmatrix} 1 & {{- 1}/2} & {{- 1}/2} \\ 0 & {\sqrt{3}/2} & {{- \sqrt{3}}/2} \end{bmatrix}}},{T_{\alpha\beta}^{- 1} = {\frac{3}{2}T_{\alpha\beta}^{T}}}} & \left( {2a} \right) \end{matrix}$

This non-normalized Clarke's transformation could be preferred since ν_(α)=ν₁, thus preserving the amplitude and phase angle of ν_(α) equal to those of ν₁. However, the algorithm is not restricted to this transformation, and thus the following normalized transformation could be used as well without further changes in the scheme.

$\begin{matrix} {{T_{\alpha\beta} = {\sqrt{\frac{2}{3}}\begin{bmatrix} 1 & {{- 1}/2} & {{- 1}/2} \\ 0 & {\sqrt{3}/2} & {{- \sqrt{3}}/2} \end{bmatrix}}},{T_{\alpha\beta}^{- 1} = T_{\alpha\beta}^{T}}} & \left( {2b} \right) \end{matrix}$

All along the document bold typeface characters represent either vectors or matrices, while normal typeface characters represent scalars.

As the unbalanced operation case is considered, then it is very convenient to describe the three-phase signal as the sum of its positive and negative sequences as given below.

ν_(αβ)=ν_(αβ) ^(p)+ν_(αβ) ^(n) =e ^(Jθ) V _(dq) ^(p) +e ^(−Jθ) V _(dq) ^(n)  (3)

where

${V_{dq}^{p} = \begin{bmatrix} V_{d}^{p} \\ V_{q}^{p} \end{bmatrix}},{V_{dq}^{n} = \begin{bmatrix} V_{d}^{n} \\ V_{q}^{n} \end{bmatrix}}$

are the vectors of coefficients, also referred as phasors, of the positive and negative sequences, respectively, of the fundamental component of ν_(α⊖). Superscripts p and n stand for the positive and negative sequences, respectively, while subscripts d and q stand for the quadrature components in the rotational frame, and the rotation matrix e^(Jθ) is given by

$\begin{matrix} {{{^{J\; \theta} = \begin{bmatrix} {\cos (\theta)} & {\sin (\theta)} \\ {- {\sin (\theta)}} & {\cos (\theta)} \end{bmatrix}},{J = \begin{bmatrix} 0 & {- 1} \\ 1 & 0 \end{bmatrix}}}{{{{where}\mspace{14mu} ^{{- J}\; \theta}} = \left( ^{J\; \theta} \right)^{T}},{J^{T} = {- {J.}}}}} & (4) \end{matrix}$

Based on description (3), the following relationship is obtained

{dot over (ν)}_(αβ) =Jωe ^(Jθ) V _(dq) ^(p) −Jωe ^(−Jθ) V _(dq) ^(n) =Jω(ν_(αβ) ^(p)−ν_(αβ) ^(n))  (5)

where {dot over (θ)}=ω represents the true angular frequency.

To get a compact form of the model it is convenient to make the following definition

φ_(αβ)

ν_(αβ) ^(p)−ν_(αβ) ^(n)  (6)

Out of which

{dot over (ν)}_(αβ)=Jωφ_(αβ)  (7)

Notice also that

{dot over (φ)}_(αβ) =Jωe ^(Jθ) V _(dq) ^(p) +Jωe ^(−Jθ) V _(dq) ^(n) =Jω(ν_(αβ) ^(p)+ν_(αβ) ^(n))=Jων _(αβ)  (8)

Summarizing, the model that completely describes the generator of signal ν_(αβ) is given by

{dot over (ν)}_(αβ)=Jωφ_(αβ)

{dot over (φ)}_(αβ)=Jων_(αβ)  (9)

It is important to remark that, in the unbalanced case, the introduction of the auxiliary variable φ_(αβ) is necessary to allow a complete description of the generator system. Recall that, in the balanced case, this description is reduced to {dot over (ν)}_(αβ)=Jων_(αβ), without the need of extra variables, as only the positive sequence is considered. In the unbalanced case, however, the negative sequence is considered in addition, which justifies the need for an extra variable φ_(αβ). In fact the following relationship between ν_(αβ) and φ_(αβ) with the positive and negative sequences can be established based on definitions (3) and (6)

$\begin{matrix} {\begin{bmatrix} v_{\alpha\beta} \\ \phi_{\alpha\beta} \end{bmatrix} = {\begin{bmatrix} I_{2} & I_{2} \\ I_{2} & {- I_{2}} \end{bmatrix}\begin{bmatrix} v_{\alpha\beta}^{p} \\ v_{\alpha\beta}^{n} \end{bmatrix}}} & (10) \end{matrix}$

whose inverse is given by

$\begin{matrix} {\begin{bmatrix} v_{\alpha\beta}^{p} \\ v_{\alpha\beta}^{n} \end{bmatrix} = {{\frac{1}{2}\begin{bmatrix} I_{2} & I_{2} \\ I_{2} & {- I_{2}} \end{bmatrix}}\begin{bmatrix} v_{\alpha\beta} \\ \phi_{\alpha\beta} \end{bmatrix}}} & (11) \end{matrix}$

where I₂ is the 2×2 identity matrix.

As previously mentioned, one of the objectives in this work consists in estimating the true angular frequency ω based on the model (9). However, as observed in (9), this parameter appears in both rows of the system. A reparameterization process is thus necessary to concentrate this parameter in a single entry. This can be solved by using the following transformation

$\begin{matrix} {\psi_{\alpha\beta} = \frac{\phi_{\alpha\beta}}{\omega}} & (12) \end{matrix}$

which yields the following model

{dot over (ν)}_(αβ)=Jω²ψ_(αβ)

{dot over (ψ)}_(αβ)=Jν_(αβ)  (13)

Moreover, it is convenient to define

σ

ω²  (14)

Out of which the model can be rewritten as

{dot over (ν)}_(αβ)=Jσψ_(αβ)

{dot over (ψ)}_(αβ)=Jν_(αβ)  (15)

which contains only a single parameter σ to estimate.

Based on system (15), the objective consists in designing:

(i) An estimator for state variables ν_(αβ) and ψ_(αβ), out of which the positive sequence component ν_(αβ) ^(p) can be obtained according to (11). (ii) An adaptive law to reconstruct parameter σ, out of which the angular frequency ω can be obtained according to (14).

Estimation of the positive sequence component. The proposed estimator consists in a copy of the system model to which a damping term is added, that is,

{circumflex over ({dot over (ν)}_(αβ) =J{circumflex over (σ)}{circumflex over (ψ)} _(αβ)+λ(ν_(αβ)−{circumflex over (ν)}_(αβ))

{circumflex over ({dot over (ψ)}_(αβ)=J{circumflex over (ν)}_(αβ)  (16)

where {circumflex over (ν)}_(αβ) and {circumflex over (ψ)}_(αβ) represent the estimates of ν_(αβ) and ψ_(αβ), respectively; {circumflex over (σ)} is the estimate of parameter σ; and λ is a positive design parameter used to introduce damping.

The error model turns out to be

{tilde under ({dot over (ν)}_(αβ=) Jσψ _(αβ) −J{circumflex over (σ)}{circumflex over (ψ)} _(αβ)−λ{tilde under (ν)}_(αβ)

{tilde under ({dot over (ψ)}_(αβ)=J{tilde under (ν)}_(αβ)  (17)

where {tilde under (ν)}_(αβ)

ν_(αβ)−{circumflex over (ν)}_(αβ), {tilde under (ψ)}_(αβ)

ψ_(αβ)−{circumflex over (ψ)}_(αβ) have been defined.

After some straightforward manipulations the error model can be rewritten as

{tilde under ({dot over (ν)}_(αβ) =Jσ{tilde under (ψ)} _(αβ) −λ{tilde under (ν)} _(αβ)

{tilde under ({dot over (ψ)}_(αβ)=J{tilde under (ν)}_(αβ)  (18)

where {tilde under (σ)}□{circumflex over (σ)}−σ has been defined.

According to (11) and (14) the positive and negative sequences can be reconstructed as follows

$\begin{matrix} {\begin{bmatrix} {\hat{v}}_{\alpha\beta}^{p} \\ {\hat{v}}_{\alpha\beta}^{n} \end{bmatrix} = {{{\frac{1}{2}\begin{bmatrix} I_{2} & I_{2} \\ I_{2} & {- I_{2}} \end{bmatrix}}\begin{bmatrix} {\hat{v}}_{\alpha\beta} \\ {\hat{\phi}}_{\alpha\beta} \end{bmatrix}} = {{\frac{1}{2}\begin{bmatrix} I_{2} & I_{2} \\ I_{2} & {- I_{2}} \end{bmatrix}}\begin{bmatrix} {\hat{v}}_{\alpha\beta} \\ {\hat{\omega}{\hat{\psi}}_{\alpha\beta}} \end{bmatrix}}}} & (19) \end{matrix}$

Estimation of the angular frequency. The design of the adaptive law to reconstruct {circumflex over (σ)} (and thus {circumflex over (ω)}=√{square root over ({circumflex over (σ)}) follows the Lyapunov approach. For this purpose the following quadratic storage function is proposed

$\begin{matrix} {W = {\frac{{\overset{\sim}{v}}_{\alpha\beta}^{T}{\overset{\sim}{v}}_{\alpha\beta}}{2} + \frac{\sigma \; {\overset{\sim}{\psi}}_{\alpha\beta}^{T}{\overset{\sim}{\psi}}_{\alpha\beta}}{2} + \frac{{\overset{\sim}{\sigma}}^{2}}{2\gamma}}} & (20) \end{matrix}$

Its time derivative along the trajectories of the error model (18) is given by

$\begin{matrix} \begin{matrix} {\overset{.}{W} = {{{\overset{\sim}{v}}_{\alpha\beta}^{T}J\; \sigma \; {\overset{\sim}{\psi}}_{\alpha\beta}} - {\lambda \; {\overset{\sim}{v}}_{\alpha\beta}^{T}{\overset{\sim}{v}}_{\alpha\beta}} - {{\overset{\sim}{v}}_{\alpha\beta}^{T}J\; \overset{\sim}{\sigma}{\overset{\sim}{\psi}}_{\alpha\beta}} + {\sigma \; {\overset{.}{\overset{\sim}{\psi}}}_{\alpha\beta}^{T}{\overset{\sim}{\psi}}_{\alpha\beta}} + {\frac{\overset{\sim}{\sigma}}{\gamma}\overset{.}{\overset{\sim}{\sigma}}}}} \\ {= {{{- \lambda}\; {\overset{\sim}{v}}_{\alpha\beta}^{T}{\overset{\sim}{v}}_{\alpha\beta}} - {{\overset{\sim}{v}}_{\alpha\beta}^{T}J\; \overset{\sim}{\sigma}{\hat{\psi}}_{\alpha\beta}} + {\frac{\overset{\sim}{\sigma}}{\gamma}\overset{.}{\overset{\sim}{\sigma}}}}} \end{matrix} & (21) \end{matrix}$

which is made negative semidefinite by proposing the following adaptive law

{tilde under ({dot over (σ)}=γ{tilde under (ν)}_(αβ) ^(T)J{circumflex over (ψ)}_(αβ)  (22)

where γ is a positive design parameter. This yields

{dot over (W)}=−λ{tilde under (ν)} _(αβ) ^(T){tilde under (ν)}_(αβ)  (23)

The stability and convergence proof can be completed using Lasalle's arguments, or using the properties of signals as follows. From (22) all error signals are bounded, i.e., {tilde under (ν)}_(αβ)εL_(∞), {tilde under (ψ)}_(αβ)εL_(∞), {tilde under (σ)}εL_(∞), equivalently all estimates are bounded as well, i.e., {circumflex over (ν)}_(αβ)εL_(∞), {circumflex over (ψ)}_(αβ)εL_(∞), {circumflex over (σ)}εL_(∞). This implies that the time derivative of the error is bounded as well, i.e., {tilde under ({dot over (ν)}_(αβ)εL_(∞) (or equivalently {circumflex over ({dot over (ν)}_(αβ)εL_(∞)). As {tilde under ({dot over (ν)}_(αβ) is continuous and bounded, and based on the fact that {tilde under (ν)}_(αβ)εL₂∩L_(∞), then {tilde under (ν)}_(αβ)→0 and {tilde under ({dot over (ν)}_(αβ)→0 as t→∞. This implies, in their turn, that {tilde under ({dot over (ψ)}_(αβ)→0 and {tilde under (σ)}→0 as t→∞ and thus {tilde under (ψ)}_(αβ) and {tilde under (σ)} (or equivalently {circumflex over (σ)}) go to constant values. Considering {tilde under (ν)}_(αβ)≡0, then from (18) σ{tilde under (ψ)}_(αβ)={tilde under (σ)}{circumflex over (ψ)}_(αβ), however, {tilde under (σ)}{circumflex over (ψ)}_(αβ) is a rotating vector while simultaneously the product σ{tilde under (ψ)}_(αβ) is a constant, hence, the only possibility is that {tilde under (σ)}→0 and {tilde under (ψ)}_(αβ)→0 as well. Notice that, no linearization process has been involved at all, and thus, both stability and convergence might be stated globally. This is, however, not fully true, as the operative region is bounded due to physical construction limitations.

Based on the fact that {tilde under ({dot over (σ)}={circumflex over ({dot over (σ)}, the adaptive law (21) is equivalent to

{circumflex over ({dot over (σ)}=γ{tilde under (ν)}_(αβ) ^(T)J{tilde under (ψ)}_(αβ)  (24)

which can also be rewritten as

{circumflex over ({dot over (σ)}=−γ{tilde under (ψ)}_(αβ) ^(T) J{tilde under (ν)} _(αβ)  (25)

Summarizing, the proposed estimator plus the adaptive law are given by

{circumflex over ({dot over (ν)}_(αβ) =J{circumflex over (σ)}{circumflex over (ψ)} _(αβ)+λ{tilde under (ν)}_(αβ)

{circumflex over ({dot over (ψ)}_(αβ)=J{circumflex over (ν)}_(αβ)  (26)

{circumflex over ({dot over (σ)}=γ{tilde under (ν)}_(αβ) ^(T)J{circumflex over (ψ)}_(αβ)  (27)

where the angular frequency can be obtained as follows

{circumflex over (ψ)}=√{square root over ({circumflex over (σ)}  (28)

And thus the positive and negative sequences can be reconstructed by means of the following inverse transformation

$\begin{matrix} {\begin{bmatrix} {\hat{v}}_{\alpha\beta}^{p} \\ {\hat{v}}_{\alpha\beta}^{n} \end{bmatrix} = {{\frac{1}{2}\begin{bmatrix} I_{2} & I_{2} \\ I_{2} & {- I_{2}} \end{bmatrix}}\begin{bmatrix} {\hat{v}}_{\alpha\beta} \\ {\hat{\omega}{\hat{\psi}}_{\alpha\beta}} \end{bmatrix}}} & (29) \end{matrix}$

Each of these estimated positive and negative sequence vectors or their combination can now be transformed to the original three-phase coordinates by using the inverse Clarke's transformation (2 a) (or (2 b) accordingly).

Design criteria for the FRF-PLL algorithm parameters. A first approximation for the tuning of parameters λ and γ is based on the desired bandwidth frequency ω_(BW) of the FRF-PLL frequency response. As the algorithm involves the computation of projections between vectors of sinusoidal signals producing mainly a second order harmonic, then the bandwidth of the algorithm should be limited by 2ω. It is common in practice to select the bandwidth as 1/10 of such frequency, that is, ω_(BW)=ω/5. Based on this consideration, the parameters can be tuned according to the following expressions

$\begin{matrix} {{\lambda = {2\omega_{BW}}}{\gamma = {2\left( \frac{\omega_{0}\omega_{BW}}{v_{\alpha\beta}} \right)^{2}}}} & (30) \end{matrix}$

where ω₀ is the nominal value of the angular frequency of the source voltage.

In the numerical results the following parameters have been selected λ=300 and λ=2.2×10⁵, which correspond approximately to a bandwidth of 24 Hz (ω_(BW)=150 rad/s), considering a nominal frequency of 50 Hz (ω₀=314.16 rad/s).

For the numerical results the following cases have been considered for the utility voltage source:

(i) Balanced condition. the voltage source is formed only by a positive sequence of 100 V of amplitude and angular frequency of 50 Hz (314.16 rad/s), with a zero phase shift. (ii) Unbalanced condition: the voltage source includes both a positive and a negative sequence components. The positive sequence has 100 V of amplitude at 50 Hz (314.16 rad/s) and zero phase shift. For the negative sequence an amplitude of 30 V and zero phase shift are considered. (iii) Unbalanced and distorted condition: harmonics 3rd and 5th are added to the previous unbalanced signal to create a periodic distortion. Both harmonics have also a negative sequence to allow unbalance. Both the positive and the negative sequence have 10 V of amplitude and zero phase shift. (iv) Utility frequency changes. The frequency of the utility voltages is step changed from 50 Hz (314.16 rad/s) to 35 Hz (219.9 rad/s) and back.

DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts the connections diagram of the proposed FRF-PLL algorithm to the voltage source. The system includes a coordinates transformation processor 104 receiving source voltages 101, 102 and 103 corresponding to the first, second, and third phase, respectively. The output of the coordinates transformation processor 105 is coupled with the estimator and adaptive processors block 106. The estimator and adaptive processors block estimates the source voltage 107 in αβ-coordinates, auxiliary variable 108 and the angular frequency 112. The positive and negative sequence components, 110 and 111, respectively, can be recovered by the signal reconstruction processor 109.

FIG. 2 depicts a detailed block diagram of the proposed algorithm. Input ports 201, 202 and 203 receive the voltages in three-phase coordinates. The coordinates transformation processor 204, contains the (normalized or non-normalized) Clarke's transformation to convert from three-phase to stationary frame coordinates (123-coordinates to αβ-coordinates). The output port 205 of this module is the voltage vector in αβ-coordinates.

The error signal vector {tilde under (ν)}_(αβ) is computed in adder 209 whose inputs are the voltage vector 205 and the estimated voltage vector 206, both in αβ-coordinates. This error signal vector is multiplied by a predetermined constant λ in 210. The output of module 210 is added in adder 211 to a vector obtained in the multiplier module 216. The resultant vector from adder 211 is operated by the matricial integrator module 212 providing the estimated voltage vector. The estimated voltage vector is sent to output port 206, and to adder 209. The estimated voltage vector is also operated by the matrix in 213. The vector obtained in module 213 is integrated in the matricial integrator module 214 providing an estimated auxiliary variable. The estimated auxiliary variable is coupled to the output port 207, and to the matrix 215. The output vector of matrix module 215 is coupled to multiplier 216.

The error signal vector {tilde under (ν)}_(αβ) is also provided to the inner product (or dot product) module 217 to obtain the inner product between the error signal vector {tilde under (ν)}αβ and the vector provided by the matrix module 215. This product is multiplied by a predetermined constant γ in 218 and then integrated in the scalar integrator module 219. The output of integrator module 219 is coupled to multiplier 216 and also it is coupled with the root module 220. The output of the root module 220 is the estimated angular frequency obtained in the adaptive processor which is sent to output port 208.

The estimated angular frequency 208 and auxiliary vector 207 are multiplied in the multiplier module 221. This product vector is added to the estimated voltage vector 206 in adder 222 and subtracted from the estimated voltage vector in adder 223. The outputs of these adders are multiplied by predetermined gains ½ in 224 and 225, respectively. The outputs of 224 and 225 modules are the positive sequence component ν_(αβ) ^(p) and the negative sequence component ν_(αβ) ^(n), respectively, which are sent to output ports 226 and 227, respectively.

FIG. 3 shows the transient response obtained with the conventional SRF-PLL algorithm when the utility voltage 301 goes from a balanced to an unbalanced operation condition at time t=3 s. In general, it is observed a considerable fluctuation in most signals due to the impossibility of the conventional SRF-PLL scheme to deal with the unbalance condition. Moreover, the SRF-PLL has been tuned, following standard guidelines, to have a high bandwidth, which also contributes to the considerable deterioration of signals. For instance, notice that, after the unbalanced condition is started at 3 s, the estimated phase angle {circumflex over (θ)} 302 (solid line) is considerably distorted compared with the true phase angle θ=ωt (dashed line) 303. It is observed also that the estimated angular frequency {circumflex over (ω)} 304 gets a considerable fluctuation around its reference 305 fixed to 314.16 rad/s. Similarly, the estimated positive sequence voltages in the synchronous frame ν_(d) ^(p) and ν_(q) ^(p), 306 and 307, respectively, show a fluctuation at twice the fundamental frequency. As the SRF-PLL has been tuned to have a high bandwidth, the q component of the voltage ν_(q) ^(p) achieves a nearly zero value.

FIG. 4 shows the transient response obtained with the proposed FRF-PLL algorithm when the utility voltage 401 goes from a balanced to an unbalanced operation condition at time t=3 s. Notice that, after a relatively short transient, all signals return to their desired values. For instance, it is observed that the estimated phase angle {circumflex over (θ)} (solid line) 402 follows perfectly well the true phase angle θ=ωt (dashed line) 403 after an almost imperceptible transient. The estimated frequency {circumflex over (ω)} (solid line) 404 is also maintained in its reference fixed to 316.14 rad/s (dotted line) 405 after a small transient. Moreover, the estimated positive-sequence voltages ν₁₂₃ ^(p) 406 have an almost imperceptible variation.

FIG. 5 presents the transient response of the proposed FRF-PLL to an unbalanced and distorted utility voltage 501 and during a start-up operation. It is shown that after a relatively small transient the estimated phase angle {circumflex over (θ)} (solid line) 502 is synchronized to the true phase angle θ=ωt (dashed line) 503. Similarly, the estimated angular frequency {circumflex over (ω)} reaches, after a small transient, its reference (dotted line) 505, which has been fixed to 316.14 rad/s. The bottom plot shows that the estimated positive-sequence voltages ν₁₂₃ ^(p) 506 reach their final amplitude of 100 V after a few cycles.

FIG. 6 shows the transient response of the proposed FRF-PLL to a step change in the angular frequency of the source voltage 601 going from 50 Hz to 35 Hz. It is shown that after a short transient the estimated phase angle {circumflex over (θ)} 602 follows perfectly well the true phase angle θ=ωt 603. It is shown that the estimated angular frequency {circumflex over (ω)} 604, starting at a reference 605 of 314.16 rad/s, reaches its new reference 606 fixed to 219.9 rad/s in a relatively short time. The bottom plot shows that the estimated positive-sequence voltages ν₁₂₃ ^(p) 607 maintain their amplitude after an almost imperceptible transient.

FIG. 7 depicts the transient response of the proposed FRF-PLL to a step change in the angular frequency of the source voltage 701 going from 35 Hz to 50 Hz. As in FIG. 6, the estimated phase angle {circumflex over (θ)} 702 follows perfectly well the true phase angle θ=ωt 703 after a short transient. It is shown that the estimated angular frequency {circumflex over (ω)} 704, starting at a reference 705 of 219.9 rad/s, reaches its new reference 706 fixed to 314.16 rad/s in a relatively short time. The bottom plot shows that the estimated positive-sequence voltages ν₁₂₃ ^(p) 707 maintain their amplitude after an almost imperceptible transient. 

1. A fixed reference frame phase locked loop (FRF-PLL) algorithm providing estimates for the angular frequency and both positive and negative sequence of the fundamental component of an unbalanced three-phase signal, both sequences in fixed reference frame coordinates, the FRF-PLL coupled to a three-phase power distribution system, the three-phase power distribution system providing a voltage for each phase, the FRF-PLL algorithm comprising: a voltage module operative to provide a signal indicative of the value of the voltage in each phase; a coordinates transformation processor coupled to voltage module operative to convert the measured three-phase voltage vector value into a voltage vector value in αβ-coordinates; an estimator processor receiving the voltage vector value ν_(αβ) in αβ-coordinates, the estimator processor configured and arranged to provide the estimate voltage vector {circumflex over (ν)}_(αβ) and the auxiliary variable {circumflex over (ψ)}_(αβ), both in αβ-coordinates; an adaptive processor receiving the error signal vector {tilde under (ν)}_(αβ), the adaptive processor configured and arranged to provide the estimated angular frequency {circumflex over (ω)}; a signal reconstruction processor receiving the estimate voltage vector {circumflex over (ν)}_(αβ), the auxiliary variable {circumflex over (ω)}_(αβ) and the estimated angular frequency {circumflex over (ω)}, the signal reconstruction processor configured and arranged to provide both estimated positive and negative sequences {circumflex over (ν)}_(αβ) ^(p) and {circumflex over (ν)}_(αβ) ^(n), respectively, in αβ-coordinates.
 2. The FRF-PLL algorithm of claim 1 wherein the voltage module is a set of three voltage sensors.
 3. The FRF-PLL algorithm of claim 1 wherein the coordinates transformation processor is the Clarke's transformation, either normalized or non normalized expressed by $T_{\alpha\beta} = {\sqrt{\frac{2}{3}}\begin{bmatrix} 1 & {{- 1}/2} & {{- 1}/2} \\ 0 & {\sqrt{3}/2} & {{- \sqrt{3}}/2} \end{bmatrix}}$ or ${T_{\alpha\beta} = {\frac{2}{3}\begin{bmatrix} 1 & {{- 1}/2} & {{- 1}/2} \\ 0 & {\sqrt{3}/2} & {{- \sqrt{3}}/2} \end{bmatrix}}},$ respectively.
 4. The FRF-PLL algorithm of claim 1 wherein the estimator processor includes: a first adder module forming a first difference vector {tilde under (ν)}_(αβ) equal to the difference between the voltage vector ν_(αβ) and the estimated voltage vector {circumflex over (ν)}_(αβ); a first gain λ multiplying the first difference vector forming a scaled vector λ{tilde under (ν)}_(αβ); a second adder module adding the scaled vector and the vector J{circumflex over (σ)}{circumflex over (ψ)}_(αβ) to form the vector λ{tilde under (ν)}_(αβ)+J{circumflex over (σ)}{circumflex over (ψ)}_(αβ); a first matricial integrator module $\quad\begin{bmatrix} {1/s} & 0 \\ 0 & {1/s} \end{bmatrix}$ receiving the vector λ{tilde under (ν)}_(αβ)+J{circumflex over (σ)}{circumflex over (ψ)}_(αβ) and forming the estimated voltage vector {circumflex over (ν)}_(αβ); a first matrix module, that represents the matrix ${J = \begin{bmatrix} 0 & {- 1} \\ 1 & 0 \end{bmatrix}},$ operating on the estimated voltage vector forming the vector J{circumflex over (ν)}_(αβ); a second matricial integrator module $\quad\begin{bmatrix} {1/s} & 0 \\ 0 & {1/s} \end{bmatrix}$ operating on the vector J{circumflex over (ν)}_(αβ) forming the auxiliary variable {circumflex over (ψ)}_(αβ); a second matrix module, that represents the matrix ${J = \begin{bmatrix} 0 & {- 1} \\ 1 & 0 \end{bmatrix}},$ operating over the auxiliary variable {circumflex over (ψ)}_(αβ) forming the vector J{circumflex over (ψ)}_(αβ); a first multiplier module multiplying the vector J{circumflex over (ψ)}_(αβ) with the scalar variable {circumflex over (σ)} forming J{circumflex over (σ)}{circumflex over (ψ)}_(αβ).
 5. The FRF-PLL algorithm of claim 1 wherein the adaptive processor includes: a first inner product module forming the inner product (or dot product) between the error signal vector {tilde under (ν)}_(αβ) and the vector J{circumflex over (ψ)}_(αβ), that is, ν_(αβ) ^(T)J{circumflex over (ψ)}_(αβ); a first gain γ multiplying the inner product forming a scaled inner product γν_(αβ) ^(T)J{circumflex over (ψ)}_(αβ); a scalar integrator module receiving the scaled inner product to form the variable {circumflex over (σ)}; a root module receiving the variable {circumflex over (σ)} and forming the estimated angular frequency {circumflex over (ω)}.
 6. The FRF-PLL algorithm of claim 1 wherein the signal reconstruction processor includes: a first multiplier module receiving the estimated angular frequency {circumflex over (ω)} and the auxiliary variable {circumflex over (ψ)}_(αβ) forming the vector {circumflex over (ω)}{circumflex over (ψ)}_(αβ); a first adder module adding the estimated voltage vector {circumflex over (ν)}_(αβ) and the vector {circumflex over (ω)}{circumflex over (ψ)}_(αβ); a second adder module subtracting {circumflex over (ω)}{circumflex over (ψ)}_(αβ) from the estimated voltage vector {circumflex over (ν)}_(αβ); a first gain multiplying the result of the first adder module forming the estimated positive sequence voltage {circumflex over (ν)}_(αβ) ^(p); a second gain multiplying the result of the second adder module forming the estimated negative sequence voltage {circumflex over (ν)}_(αβ) ^(n).
 7. The estimator processor of claim 4 wherein the gain λ is a predetermined design constant that is greater than zero, and a first approximation is λ=2ω_(BW).
 8. The adaptive processor of claim 5 wherein the gain γ is a predetermined design constant that is greater than zero, and a first approximation is $\gamma = {2{\left( \frac{\omega_{0}\omega_{BW}}{v_{\alpha\beta}} \right)^{2}.}}$
 9. The estimator processor of claim 4 wherein the definition of the auxiliary variable {circumflex over (ψ)}_(αβ) permits the concentration of the angular frequency parameter ω in a single entry, facilitating the design of the adaptive algorithm to estimate the angular frequency ω. 